Multi-sensor electro-optical (EO) systems may require the fusion of two or more images, e.g., so as to improve overall image quality or detail.
Traditional fusion systems employ complex processing to obtain proper alignment between the different input images. This may include image processing techniques of feature matching to calculate disparities. Such methods are computationally expensive and are not suitable for low power systems (e.g., EP1797523 A2)
Complex processing consumes a lot of power, which severely limits the portability of such systems, e.g. in a handheld unit. The presence of large batteries once again makes the device extremely cumbersome
Digital methods of image alignment are imperfect, especially when dealing with multi-spectral input. For instance, the input from an infrared sensor and day sensor would contain very different texture information, making accurate registration difficult.
Heavy processing requirements drive up the cost for commercial units
Mechanical methods of aligning sensors to eliminate angular (yaw, pitch, roll) errors between them also suffer from inaccuracies. Moreover, mechanical systems tend to lose alignment over time, especially during rugged outdoor use, and re-alignment of the sensors is very difficult. The presence of multiple components that have to be accurately aligned (lens assembly, mirrors, CCD sensors etc.) compounds the problem even more.